• Genomics & Informatics
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• v.21 no.1
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• pp.6.1-6.8
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• 2023

Glaucoma is the second leading cause of irreversible blindness, and primary open-angle glaucoma (POAG) is the most common type. Due to inadequate diagnosis, treatment is often not administered until symptoms occur. Hence, approaches enabling earlier prediction or diagnosis of POAG are necessary. We aimed to identify novel drugs for glaucoma through bioinformatics and network analysis. Data from 36 samples, obtained from the trabecular meshwork of healthy individuals and patients with POAG, were acquired from a dataset. Next, differentially expressed genes (DEGs) were identified to construct a protein-protein interaction (PPI) network. In both stages, the genes were enriched by studying the critical biological processes and pathways related to POAG. Finally, a drug-gene network was constructed, and novel drugs for POAG treatment were proposed. Genes with p < 0.01 and |log fold change| > 0.3 (1,350 genes) were considered DEGs and utilized to construct a PPI network. Enrichment analysis yielded several key pathways that were upregulated or downregulated. For example, extracellular matrix organization, the immune system, neutrophil degranulation, and cytokine signaling were upregulated among immune pathways, while signal transduction, the immune system, extracellular matrix organization, and receptor tyrosine kinase signaling were downregulated. Finally, novel drugs including metformin hydrochloride, ixazomib citrate, and cisplatin warrant further analysis of their potential roles in POAG treatment. The candidate drugs identified in this computational analysis require in vitro and in vivo validation to confirm their effectiveness in POAG treatment. This may pave the way for understanding life-threatening disorders such as cancer.

• Asian-Australasian Journal of Animal Sciences
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• v.16 no.10
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• pp.1411-1420
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• 2003

Comparative maps, representing chromosomal locations of homologous genes in different species, are useful sources of information for identifying candidate disease genes and genes determining complex traits. They facilitate gene mapping and linkage prediction in other species, and provide information on genome organization and evolution. Here, the current gene mapping and comparative mapping status of the major livestock species are presented. Two techniques were widely used in comparative mapping: FISH (Fluorescence In Situ Hybridization) and PCR-based mapping using somatic cell hybrid (SCH) or radiation hybrid (RH) panels. New techniques, using, for example, ESTs (Expressed Sequence Tags) or CASTS (Comparatively Anchored Sequence Tagged Sites), also have been developed as useful tools for analyzing comparative genome organization in livestock species, further enabling accurate transfer of valuable information from one species to another.

• Genomics & Informatics
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• v.14 no.4
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• pp.166-172
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• 2016

Although a large number of genetic variants have been identified to be associated with common diseases through genome-wide association studies, there still exits limitations in explaining the missing heritability. One approach to solving this missing heritability problem is to investigate gene-gene interactions, rather than a single-locus approach. For gene-gene interaction analysis, the multifactor dimensionality reduction (MDR) method has been widely applied, since the constructive induction algorithm of MDR efficiently reduces high-order dimensions into one dimension by classifying multi-level genotypes into high- and low-risk groups. The MDR method has been extended to various phenotypes and has been improved to provide a significance test for gene-gene interactions. In this paper, we propose a simple method, called accelerated failure time (AFT) UM-MDR, in which the idea of a unified model-based MDR is extended to the survival phenotype by incorporating AFT-MDR into the classification step. The proposed AFT UM-MDR method is compared with AFT-MDR through simulation studies, and a short discussion is given.

• Genomics & Informatics
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• v.17 no.3
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• pp.31.1-31.7
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• 2019

We sought the novel concept, transcript capacity (TC) and analyzed TC. Our approach to estimate TC was through an in silico method. TC refers to the capacity that a transcript exerts in a cell as enzyme or protein function after translation. We used the genome-wide association study (GWAS) beta effect and transcription level in RNA-sequencing to estimate TC. The trait was body fat percent and the transcript reads were obtained from the human protein atlas. The assumption was that the GWAS beta effect is the gene's effect and TC was related to the corresponding gene effect and transcript reads. Further, we surveyed gene ontology (GO) in the highest TC and the lowest TC genes. The most frequent GOs with the highest TC were neuronal-related and cell projection organization related. The most frequent GOs with the lowest TC were wound-healing related and embryo development related. We expect that our analysis contributes to estimating TC in the diverse species and playing a benevolent role to the new bioinformatic analysis.

• The Korea Genome Conference
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• 2006.09a
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• pp.45-45
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• 2006

• The Korea Genome Conference
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• 2006.09a
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• pp.69-69
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• 2006

• The Korea Genome Conference
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• 2006.09a
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• pp.59-59
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• 2006

• Proceedings of the Zoological Society Korea Conference
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• 1998.10b
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• pp.335.1-335
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• 1998

No Abstract, See Full Text

• The Korea Genome Conference
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• 2004.07a
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• pp.34.1-34.1
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• 2004

• The Korea Genome Conference
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• 2003.09a
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• pp.38.1-38.1
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• 2003